Python has been dominating the ETL space for a few years now. There are easily more than a hundred Python ETL Tools that act as Frameworks, Libraries, or Software for ETL. Why so? Because it’s versatile, easy to use, and has a rich ecosystem of libraries and tools.
Python also has a large community to help you out with ETL in Python. Its quick development cycle enables you to rapid prototyping of ETL processes. Now, the question is, do you want to build pipelines in-house or adopt an ETL using Python? I will leave that to you. You can decide after weighing the pros and cons of the ETL tools in this blog.
Let’s get started!
What Are Python ETL Tools?
Python ETL Tools are the general ETL Tools written in Python and support other Python libraries for extracting, loading, and transforming different types of tables of data imported from multiple data sources like XML, CSV, Text, or JSON, etc. into Data Warehouses, Data Lakes, etc. Python is a widely used language to create data pipelines and is easy to manage. Python ETL Tools are fast, reliable, and deliver high performance.
Significance of Python ETL Tools
Some of the reasons for using Python ETL tools are:
- If you want to code your own tool for ETL and are comfortable with programming in Python.
- Your ETL requirements are simple and easily executable.
- You have very specific requirements, and can only go about ETL using Python.
Top Python ETL Tools
In this section, you will explore the various Python ETL Tools.
Here is a list of the 9 best Python ETL tools.
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1) Python ETL Tool: Apache Airflow
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Apache Airflow is an Open Source automation Tool built on Python used to set up and maintain Data Pipelines. Technically, Airflow is not an ETL Tool but rather lets you organize and manage your ETL Pipelines using DAGs (Directed Acyclic Graphs). DAGs lets you run a single branch more than once or even skip branches in your sequence when necessary.
A typical Airflow setup will look something like this:
Metadata database > Scheduler > Executor > Workers
The Metadata Database stores your workflows/tasks, the Scheduler, which runs as a service uses DAG definitions to choose tasks and the Executor decides which worker executes the task. Workers execute the logic of your workflow/task.
Apache Airflow can seamlessly integrate with your existing ETL toolbox since it’s incredibly useful for Management and Organization. Apache Airflow makes sense when you want to perform long ETL jobs or your ETL has multiple steps, Airflow lets you restart from any point during the ETL process. However, it should be clear that Apache Airflows isn’t a library, so it needs to be deployed and therefore, may not be suitable for small ETL jobs.
One key element of Airflow is that you can easily manage all of DAG’s workflows via the Airflow WebUI. This means that you can schedule automated workflows without having to manage and maintain them. You will also be able to execute it using a Command-Line Interface.
2) Python ETL Tool: Luigi
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Luigi is also an Open Source Python ETL Tool that enables you to develop complex Pipelines. It has a number of benefits which include good Visualization Tools, Failure Recovery via Checkpoints, and a Command-Line Interface.
The main difference between Luigi and Airflow is in the way the Dependencies are specified and the Tasks are executed. Luigi works with Tasks and Targets.
Tasks utilize the Targets, which are produced by a finished Task. So, a Task will remove a Target, then another Task will consume that Target and remove another one. This allows the whole process to be straightforward, and workflows to be simple. This is right for simple ETL Tasks but not complex Tasks.
Luigi is your best choice if you want to automate simple ETL processes like Logging. It is important to note that with Luigi you cannot interact with the different processes. Also, Luigi does not automatically sync Tasks to workers for you. It does not provide the facility to Schedule, Alert or Monitor as Airflow would.
3) Python ETL Tool: Pandas
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Pandas is a Python library that provides you with Data Structures and Analysis Tools. It simplifies ETL processes like Data Cleansing by adding R-style Data Frames. However, it is time-consuming as you would have to write your own code. It can be used to write simple scripts easily, and is one of the widely used Python ETL tools.
However, when it comes to in-memory and scalability, Pandas’ performance may not keep up with expectations.
You should use Pandas when you need to rapidly extract data, clean and transform it, and write it to an SQL Database/Excel/CSV. Once you start working with large data sets, it usually makes more sense to use a more scalable approach.
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Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.
4) Python ETL Tool: Bonobo
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Bonobo is lightweight and easy to use. You will be able to deploy pipelines rapidly and in parallel. Bonobo can be used to extract data from multiple sources in different formats including CSV, JSON, XML, XLS, SQL, etc. This transformation follows atomic UNIX principles. One of the best qualities about Bonobos is that new users will not have to learn a new API. It is especially easy to use if you have a background in Python. It also has the ability to handle semi-complex schemas. One of the biggest plus points is that it’s Open Source and scalable.
Bonobo is suitable when you need simple, lightweight ETL jobs done, and you don’t have the time to learn a new API. One more key point to note is that Bonobo has an official Docker that lets you run jobs within Docker Containers. Moreover, it allows CLI execution as well.
5) Python ETL Tool: petl
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petl is an aptly named Python ETL solution. You can extract data from multiple sources and build tables. It is quite similar to Pandas in the way it works, although it doesn’t quite provide the same level of analysis. petl is able to handle very complex datasets, leverage system memory, and can scale easily too. The best use case for using petl is when you want the basics of ETL without the analytics and the job is not time-sensitive.
6) Python ETL Tool: PySpark
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Among all the Python ETL tools, PySpark has one of the most versatile interfaces designed for Apache Spark that allows users to use Python APIs to write Spark applications. It is needed because Apache Spark is written in Scala language, and to work with Apache Spark using Python, an interface like PySpark is required.
PySpark helps users connect with Resilient Distributed Datasets (RDDs) to Apache Spark and Python. PySpark supports most of Apache Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core.
7) Python ETL Tool: Odo
Odo is a Python tool that converts data from one format to another and delivers high performance while loading huge datasets into various datasets. It includes in-memory structures like NumPy array, data frames, lists, etc. Users should try Odo if they looking to make simple pipelines but want to load large CSV datasets. It also supports data outside of Python like CSV/JSON/HDF5 files, SQL databases, data on remote machines, and the Hadoop File System.
8) Python ETL Tool: mETL
mETL is a Python ETL tool that is designed for loading elective data for CEU. It is a web-based ETL tool that allows developers to create custom components that they can run and integrate as per the data integration requirements by an organization. It can load any kind of data and comes with widespread file formats with data migration and data migration packages. Users can use mETL for service-based data integrations, flat-file integrations, publisher-subscriber data integrations, etc.
9) Python ETL Tool: Riko
Riko is a stream processing engine written in Python to analyze and process streams of structured data. Riko is best suited for handling RSS feeds as it supports parallel execution using its synchronous and asynchronous APIs. It also comes with CLI support for the execution of stream processors. It is modeled after Yahoo pipes and became its replacement and can help a lot of companies to create Business Intelligence Applications interacting as per demand with the databases of customers when connected with Data Warehouses.
How to Use Python for ETL?
Python is a versatile language that users can use to code almost any ETL process. It can easily handle indexed data structures and dictionaries, which is important in ETL operations.
With the help of Python, you can code and filter out null values from the data in a list using the pre-built Python math module. Most of the time the ETL tool is developed with a mix of pure Python code, externally defined functions, and libraries that offer great flexibility to developers such as the Pandas library to filter an entire DataFrame of rows containing nulls.
For instance, using some built-in Python modules makes it really easy to filter out null values from a list:
import math
data = [1.0, 3.0, 6.5, float('NaN'), 40.0, float('NaN')]
filtered = []
for value in data:
if not math.isnan(value):
filtered.append(value)
You can use list comprehensions to accomplish this task as well:
filtered = [value for value in data if not math.isnan(value)]
It is not very efficient to code the entire ETL process from scratch, as most of the ETL code becomes a mix of pure Python script and externally defined objects or functions. For example, you can use pandas to filter out entire data frames of rows containing null values:
filtered = data.dropna()
Software Development kits of Python, APIs, and other supports are available for easy development in Python that are highly useful in building ETL Tools
How to Select the Best Python ETL Tool
When selecting the best Python ETL tool for your data engineering
projects, choose one that:
- Covers all of the numerous data sources from which raw data can be extracted.
- Can handle sophisticated pipelines for cleaning and converting data.
- Covers all data destinations (SQL databases, data warehouses, data lakes, and filesystems) to which you will load your data.
- It can easily scale if numerous jobs are running simultaneously to save time.
- Is extensible – it can be used not just for data engineering but also by data scientists to develop complicated schemas for data science projects.
- Are easily monitored; observability is critical for debugging and ensuring data quality.
Additional Reads:
Conclusion
In this blog post, you have seen the 9 most popular Python ETL tools available in the market. The Python ETL tools you choose depend on your business needs, time constraints, and budget. The Python ETL tools we discussed are Open Source and thus can be easily leveraged for your ETL needs.
Designing a custom pipeline using the Python ETL Tools is often a time-consuming & resource intensive task. This requires you to assign a portion of your engineering bandwidth to design, develop, monitor & maintain data pipelines for a seamless data replication process.
If you’re looking for a more effective all-in-one solution, that will not only help you transfer data but also transform it into analysis-ready form, then a Cloud-Based ETL Tool like Hevo Data is the right choice for you!
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With an analytical perspective and a problem-solving approach, Shruti has extensive experience in solving business problems of data teams by delivering thoroughly researched content pivotal for data practitioners.